2 Dataset Name: Hahn1 (Hahn1.dat)
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5 Starting Values (lines 41 to 47)
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6 Certified Values (lines 41 to 52)
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7 Data (lines 61 to 296)
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9 Procedure: Nonlinear Least Squares Regression
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11 Description: These data are the result of a NIST study involving
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12 the thermal expansion of copper. The response
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13 variable is the coefficient of thermal expansion, and
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14 the predictor variable is temperature in degrees
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18 Reference: Hahn, T., NIST (197?).
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19 Copper Thermal Expansion Study.
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25 Data: 1 Response (y = coefficient of thermal expansion)
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26 1 Predictor (x = temperature, degrees kelvin)
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28 Average Level of Difficulty
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31 Model: Rational Class (cubic/cubic)
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32 7 Parameters (b1 to b7)
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34 y = (b1+b2*x+b3*x**2+b4*x**3) /
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35 (1+b5*x+b6*x**2+b7*x**3) + e
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38 Starting values Certified Values
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40 Start 1 Start 2 Parameter Standard Deviation
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41 b1 = 10 1 1.0776351733E+00 1.7070154742E-01
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42 b2 = -1 -0.1 -1.2269296921E-01 1.2000289189E-02
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43 b3 = 0.05 0.005 4.0863750610E-03 2.2508314937E-04
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44 b4 = -0.00001 -0.000001 -1.4262662514E-06 2.7578037666E-07
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45 b5 = -0.05 -0.005 -5.7609940901E-03 2.4712888219E-04
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46 b6 = 0.001 0.0001 2.4053735503E-04 1.0449373768E-05
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47 b7 = -0.000001 -0.0000001 -1.2314450199E-07 1.3027335327E-08
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49 Residual Sum of Squares: 1.5324382854E+00
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50 Residual Standard Deviation: 8.1803852243E-02
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51 Degrees of Freedom: 229
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52 Number of Observations: 236
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projects / platform / upstream / ceres-solver.git / blob